Many types of users may shop at an e-commerce website. For example, there could be corporate users (e.g., users who are buying items on behalf of companies) and individual users (e.g., users who are buying items for personal use). Corporate users can be further categorized into raw materials merchants, manufacturers, wholesalers, retailers, and traders, for example. It is likely that the product interests of typical corporate users are more consistent and focused than those of individual users since the needs of a company may not vary much over time whereas people's personal interests may vary more often. It is also likely that the product interests of raw materials merchants and manufacturers are more concentrated than those of wholesalers, retailers, and traders, for example. Being able to identify the degree of concentration of a user's product interests, the user group to which a user belongs, and how product/information queries and recommendations are to be handled for each user group would greatly improve recommendations made to a user.
In typical systems, recommendations are generally made based on a user's historical preferences and/or correlations of interests among different users. But generally, typical recommendation systems do not differentiate between different types of users. For example, the same recommendation technique may be used by a typical recommendation system for both individual users and corporate users. For example, such recommendation systems may determine certain product/information categories whose webpages are frequently visited by users and then recommend highly rated and/or new products/information from these categories to users. Such recommendation systems may determine recommendations based on product/information correlations such as, for example, recommending for a user who is browsing the webpage of a first product, a second product that is related to the first product and/or a third product that is browsed by other users that are similar to that user. Additionally, typical recommendation systems treat recommendations for different types of users similarly.